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Add support for ObjectiveFunction{ScalarQuadraticFunction} #276

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Jun 6, 2023
Merged

Add support for ObjectiveFunction{ScalarQuadraticFunction} #276

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46 changes: 32 additions & 14 deletions src/MOI_wrapper/MOI_wrapper.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -181,6 +181,7 @@ function MOI.supports(
::Union{
MOI.ObjectiveSense,
MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}},
MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}},
},
)
return true
Expand Down Expand Up @@ -258,29 +259,46 @@ function MOI.optimize!(
end

model_attributes = MOI.get(src, MOI.ListOfModelAttributesSet())
objective_function_attr =
MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Cdouble}}()
max_sense = false
obj_attr = nothing
for attr in model_attributes
if attr == MOI.ObjectiveSense() || attr == objective_function_attr
continue # These are handled later
if attr == MOI.ObjectiveSense()
max_sense = MOI.get(src, attr) == MOI.MAX_SENSE
elseif attr == MOI.Name()
continue # This can be skipped without consequence
elseif attr isa MOI.ObjectiveFunction
obj_attr = attr
else
throw(MOI.UnsupportedAttribute(attr))
end
throw(MOI.UnsupportedAttribute(attr))
end
max_sense = false
if MOI.ObjectiveSense() in model_attributes
max_sense = MOI.get(src, MOI.ObjectiveSense()) == MOI.MAX_SENSE
end
objective_constant = 0.0
c = zeros(A.n)
if objective_function_attr in model_attributes
obj = MOI.get(src, objective_function_attr)
objective_constant, c, P = 0.0, zeros(A.n), SparseArrays.spzeros(A.n, A.n)
if obj_attr == MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Cdouble}}()
obj = MOI.get(src, obj_attr)
objective_constant = MOI.constant(obj)
for term in obj.terms
c[term.variable.value] += (max_sense ? -1 : 1) * term.coefficient
end
elseif obj_attr ==
MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Cdouble}}()
obj = MOI.get(src, obj_attr)
objective_constant = MOI.constant(obj)
scale = max_sense ? -1 : 1
for term in obj.affine_terms
c[term.variable.value] += scale * term.coefficient
end
nnz = length(obj.quadratic_terms)
I, J, V = zeros(Int, nnz), zeros(Int, nnz), zeros(Cdouble, nnz)
for (i, qterm) in enumerate(obj.quadratic_terms)
I[i] = min(qterm.variable_1.value, qterm.variable_2.value)
J[i] = max(qterm.variable_1.value, qterm.variable_2.value)
V[i] = scale * qterm.coefficient
end
P = SparseArrays.sparse(I, J, V, A.n, A.n)
elseif obj_attr !== nothing
throw(MOI.UnsupportedAttribute(obj_attr))
end

# `model.primal` contains the result of the previous optimization.
# It is used as a warm start if its length is the same, e.g.
# probably because no variable and/or constraint has been added.
Expand Down Expand Up @@ -350,7 +368,7 @@ function MOI.optimize!(
A.m,
A.n,
A,
SparseArrays.spzeros(A.n, A.n), # placeholder: P
P,
Ab.constants.b,
c,
Ab.sets.num_rows[1],
Expand Down